Job Application - Quantitative Analyst

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Question I

I use daily OHLCV USDJPY data (from 2014-01-01 to 2017-01-20) and application of some models to forecast the highest and lowest price :

- Auto Arima models
- Exponential Time Series
- Univariate Garch models
- Exponential Weighted Moving Average
- Monte Carlo Markov Chain
- Bayesian Time Series
- Midas

For the staking model, I simply forecast the highest and lowest price, and then : 

- Kelly criterion and using highest or lowest price for closing transaction, otherwise using closing price if the forecasted lowest/highest price is not occur.
- Placed $100 an each of the forecasted variance value and do the settlement based on the real variance value. 

• http://rpubs.com/englianhu/binary-forex-trading-condensed-version(http://rpubs.com/englianhu/302665)
• http://rpubs.com/englianhu/304134

Question II

Question III

Reference

Question I

1. Stock Market Forecasting Using LASSO Linear Regression Model
2. Using LASSO from lars (or glmnet) package in R for variable selection
3. Difference between glmnet() and cv.glmnet() in R?
4. Testing Kelly Criterion and Optimal f in R
5. Portfolio Optimization and Monte Carlo Simulation
6. Glmnet Vignette
7. lasso怎么用算法实现？
8. The Sparse Matrix and {glmnet}
9. Regularization and Variable Selection via the Elastic Net
10. LASSO, Ridge, and Elastic Net
11. 热门数据挖掘模型应用入门（一）: LASSO回归
12. The Lasso Page
13. Call_Valuation.R
14. Lecture 6 – Stochastic Processes and Monte Carlo
15. The caret Package
16. Time Series Cross Validation
17. Character-Code.com
18. Size Matters – Kelly Optimization

Question II

Question III

1. Data APIs/feeds available as packages in R